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Outliers and Spatial Dependence in Cross-Sectional Regressions


  • Jesús Mur

    (Department of Economic Analysis, University of Zaragoza, Zaragoza, Spain)

  • Jørgen Lauridsen

    (The Econometric Group, Department of Economics, University of Southern Denmark, Odense, Denmark)


Outliers are a risk factor in any econometric analysis. They are often observations that exert an excessive influence on the results and lower our confidence in the estimations. As a consequence, the attention given to their identification and treatment in the context of time series is not surprising. Our intention in the present paper is to advance in this same direction but now focusing the discussion on the impact of outliers on cross-sectional specifications. In particular, we will analyse the behaviour of the most habitual misspecification tests in this field in the presence of outliers. With this objective, we present a series of analytical results that try to delimit the effects suffered by these statistics and complete the study with a Monte Carlo exercise designed to measure their effect more precisely. According to our results, the impact of outliers can be very important, especially when several coincide in the sample.

Suggested Citation

  • Jesús Mur & Jørgen Lauridsen, 2007. "Outliers and Spatial Dependence in Cross-Sectional Regressions," Environment and Planning A, , vol. 39(7), pages 1752-1769, July.
  • Handle: RePEc:sae:envira:v:39:y:2007:i:7:p:1752-1769

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